US-20260126346-A1 - METHOD FOR RAPID BEARING DAMAGE ANALYSIS

US20260126346A1US 20260126346 A1US20260126346 A1US 20260126346A1US-20260126346-A1

Abstract

A method for rapid bearing damage analysis, including the following steps: training model data with an algorithm to generate a modal information model, including obtaining a vibration signal from a test bearing, converting the signal into a vibration spectrum, smoothing, visualizing, and annotating the vibration spectrum with modal information, then doing image recognition training on the vibration spectrum as the model data using the algorithm. Thereafter, loading the modal information model to recognize modal information of a bearing and perform damage analysis to generate a diagnostic result. This process includes obtaining the bearing's vibration signal, converting it into a vibration spectrum, smoothing the spectrum, recognizing the modal information using the modal information model, performing band-pass filtering based on the modal information, and performing demodulation to obtain a characteristic frequency, ultimately producing the diagnostic result.

Inventors

Jenn-Kai Tsai
Chiu-Chang Chen

Assignees

NATIONAL FORMOSA UNIVERSITY

Dates

Publication Date: 20260507
Application Date: 20251021
Priority Date: 20241101

Claims (20)

1 . A method for rapid bearing damage analysis, comprising steps of: training model data with an algorithm to generate a modal information model, comprising: obtaining a vibration signal of a test bearing; converting the vibration signal into a vibration spectrum; smoothing and visualization the vibration spectrum, and annotating the vibration spectrum with modal information; and performing image recognition training on the model data using the vibration spectrum with the algorithm; and loading the modal information model to recognize the modal information of a bearing and perform bearing damage analysis to generate a diagnostic result, comprising: obtaining the vibration signal of the bearing and converting the vibration signal into the vibration spectrum; smoothing the vibration spectrum and recognizing the modal information using the modal information model; performing band-pass filtering on the vibration signal and the vibration spectrum based on the modal information; and performing demodulation to obtain a characteristic frequency and generating the diagnostic result.
2 . The method for rapid bearing damage analysis according to claim 1 , wherein the algorithm is a one-stage object detection algorithm.
3 . The method for rapid bearing damage analysis according to claim 2 , wherein the algorithm is YOLO.
4 . The method for rapid bearing damage analysis according to claim 1 , wherein the demodulation is performed using a method employing a cumulative enhanced energy operator to demodulate the modal signal.
5 . The method for rapid bearing damage analysis according to claim 2 , wherein the demodulation is performed using a method employing a cumulative enhanced energy operator to demodulate the modal signal.
6 . The method for rapid bearing damage analysis according to claim 3 , wherein the demodulation is performed using a method employing a cumulative enhanced energy operator to demodulate the modal signal.
7 . The method for rapid bearing damage analysis according to claim 4 , wherein the method of the cumulative enhanced energy operator defines the vibration signal as a function s(t), where t=n represents each sampling time point of the vibration signal, and the cumulative enhanced energy operator function CEEO(s(n)) is defined as C ⁢ E ⁢ E ⁢ O ⁢ ( s ⁡ ( n ) ) = s 2 ( n ) - s ⁡ ( n - 2 ) ⁢ s ⁡ ( n + 2 ) .
8 . The method for rapid bearing damage analysis according to claim 5 , wherein the method of the cumulative enhanced energy operator defines the vibration signal as a function s(t), where t=n represents each sampling time point of the vibration signal, and the cumulative enhanced energy operator function CEEO(s(n)) is defined as C ⁢ E ⁢ E ⁢ O ⁢ ( s ⁡ ( n ) ) = s 2 ( n ) - s ⁡ ( n - 2 ) ⁢ s ⁡ ( n + 2 ) .
9 . The method for rapid bearing damage analysis according to claim 6 , wherein the method of the cumulative enhanced energy operator defines the vibration signal as a function s(t), where t=n represents each sampling time point of the vibration signal, and the cumulative enhanced energy operator function CEEO(s(n)) is defined as C ⁢ E ⁢ E ⁢ O ⁢ ( s ⁡ ( n ) ) = s 2 ( n ) - s ⁡ ( n - 2 ) ⁢ s ⁡ ( n + 2 ) .
10 . The method for rapid bearing damage analysis according to claim 7 , wherein smoothing the vibration spectrum comprising using methods of simple moving average, weighted moving average, exponential moving average, Savitzky-Golay filtering, locally weighted regression, or wavelet smoothing.
11 . The method for rapid bearing damage analysis according to claim 8 , wherein smoothing the vibration spectrum comprising using methods of simple moving average, weighted moving average, exponential moving average, Savitzky-Golay filtering, locally weighted regression, or wavelet smoothing.
12 . The method for rapid bearing damage analysis according to claim 9 , wherein smoothing the vibration spectrum comprising using methods of simple moving average, weighted moving average, exponential moving average, Savitzky-Golay filtering, locally weighted regression, or wavelet smoothing.
13 . The method for rapid bearing damage analysis according to claim 10 , wherein smoothing the vibration spectrum uses the method of simple moving average.
14 . The method for rapid bearing damage analysis according to claim 11 , wherein smoothing the vibration spectrum uses the method of simple moving average.
15 . The method for rapid bearing damage analysis according to claim 12 , wherein smoothing the vibration spectrum uses the method of simple moving average.
16 . The method for rapid bearing damage analysis according to claim 13 , wherein the modal information comprises one or more modal signals and one or more corresponding modal positions, and the band-pass filtering retains the modal signals at the modal positions of the vibration spectrum in accordance with the modal information model.
17 . The method for rapid bearing damage analysis according to claim 14 , wherein the modal information comprises one or more modal signals and one or more corresponding modal positions, and the band-pass filtering retains the modal signals at the modal positions of the vibration spectrum in accordance with the modal information model.
18 . The method for rapid bearing damage analysis according to claim 15 , wherein the modal information comprises one or more modal signals and one or more corresponding modal positions, and the band-pass filtering retains the modal signals at the modal positions of the vibration spectrum in accordance with the modal information model.
19 . The method for rapid bearing damage analysis according to claim 18 , wherein the modal position corresponds to a bounding box of the model data.
20 . The method for rapid bearing damage analysis according to claim 19 , wherein the method of converting the vibration signal into the vibration spectrum is Fast Fourier Transform.

Description

FIELD OF INVENTION The present invention relates to a method for bearing damage analysis, and more particularly to a method for rapid bearing damage analysis. BACKGROUND OF THE INVENTION Bearings are indispensable and critical components in modern mechanical equipment. Whether in household appliances used in daily life or in large-scale machinery such as industrial equipment, automobiles, and aircraft, bearings play a vital role in supporting rotational motion, reducing friction, and enhancing operational efficiency. The proper functioning of bearings is directly related to the performance and safety of mechanical equipment. Therefore, accurately and promptly detecting and analyzing bearing damage is of the utmost importance. Among existing techniques, vibration analysis is the most commonly used method for detecting and diagnosing bearing damage, widely applied in condition monitoring (CM) of industrial equipment. This method measures vibration signals during operation using accelerometers installed near the bearing, and transforms these signals into vibration modes to identify potential damage issues. However, conventional vibration analysis methods require skilled technicians to perform data analysis and interpretation. The process is time-consuming, and identification of vibration modes must be conducted under specific rotational speeds and conditions, making full automation difficult to achieve. As a result, operational and labor costs increase significantly, and the equipment needs to be shut down during inspection, leading to a loss in production efficiency. Therefore, the development of a rapid and automated technology for bearing damage analysis and detection has become an urgent objective in the related field. SUMMARY OF THE INVENTION To develop a fast and automated bearing fault analysis and detection technology, the present invention provides a method for rapid bearing damage analysis, comprising steps of: training model data with an algorithm to generate a modal information model, comprising: obtaining a vibration signal of a test bearing; converting the vibration signal into a vibration spectrum; smoothing and visualization the vibration spectrum, and annotating the vibration spectrum with modal information; and performing image recognition training on the model data using the vibration spectrum with the algorithm; and loading the modal information model to recognize the modal information of a bearing and perform bearing damage analysis to generate a diagnostic result, comprising: obtaining the vibration signal of the bearing and converting the vibration signal into the vibration spectrum; smoothing the vibration spectrum and recognizing the modal information using the modal information model; performing band-pass filtering on the vibration signal and the vibration spectrum based on the modal information; and performing demodulation to obtain a characteristic frequency and generating the diagnostic result. Wherein, the algorithm is a one-stage object detection algorithm. Wherein, the algorithm is YOLO. Wherein, the demodulation is performed using a method employing a cumulative enhanced energy operator to demodulate the modal signal. Wherein, the method of the cumulative enhanced energy operator defines the vibration signal as a function s(t), where t=n represents each sampling time point of the vibration signal, and the cumulative enhanced energy operator function CEEO(s(n)) is defined as CEEO(s(n))=s2(n)−s(n−2)s(n+2). Wherein, smoothing the vibration spectrum comprising using methods of simple moving average, weighted moving average, exponential moving average, Savitzky-Golay filtering, locally weighted regression, or wavelet smoothing. Wherein, smoothing the vibration spectrum uses the method of simple moving average. Wherein, the modal information comprises one or more modal signals and one or more corresponding modal positions, and the band-pass filtering retains the modal signals at the modal positions of the vibration spectrum in accordance with the modal information model. Wherein, the modal position corresponds to a bounding box of the model data. Wherein, the method of converting the vibration signal into the vibration spectrum is Fast Fourier Transform. Based on the above description, it is clear that the present invention achieves the following advantages: 1. The method for rapid bearing damage analysis in accordance with the present invention can be highly automated, eliminating the need for extensive operation by specialized technicians and complex data analysis and interpretation. This significantly simplifies damage analysis, reduces labor costs, and improves analysis efficiency. 2. The method for rapid bearing damage analysis in accordance with the present invention significantly reduces the time required for damage analysis compared to prior arts, enabling real-time damage detection and monitoring, thereby greatly improving production efficiency and minimizing downtime. 3. Th